Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition

Büyükşahin, Ümit Çavuş; Ertekin, Şeyda

doi:10.1016/j.neucom.2019.05.099

Cited by 233 publications

(118 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Önerilen öznitelik tabanlı hibrit yönteminin performansı, her biri farklı alanlardan gelen ve farklı istatistiksel özelliklere sahip olan dört farklı zaman serisinde değerlendirilmiştir. Bunlardan üçü, zaman serileri tahmini modelleme literatüründe yaygın olarak kullanılan referans veri setleridir: Wolf'un güneş lekesi verileri, Kanada'da yakalanan vaşak verileri ve İngiliz sterlini/ABD doları döviz kuru verileridir [13,[16][17][18]. Dördüncü veri seti ise, halka açık olan Türkiye gün içi piyasasının elektrik fiyatıdır [32].…”

Section: Deneyler (Experiments)unclassified

“…Deneylerde, önerilen yöntemin performans sonuçları, tüm veri setleri için, üzerine inşa edilen hibrit yöntem [18]…”

Section: Deneyler (Experiments)unclassified

“…Bu hibrit yöntemler, her bir tekil yöntemin benzersiz mukavemetinin avantajlarını kullanabilmeleri için sunulmuştur. Bu sayede hibrit yöntemlerin, kullandığı tekil yöntemlerden daha iyi performans elde etikleri gösterilmiştir [13,[16][17][18]. Hibrit yöntemlerdeki yaygın uygulama şu şekildedir: Önce, belirli bir zaman serisi verisi doğrusal ve doğrusal olmayan bileşenlerine ayrıştırılır; daha sonra uygun tekil yöntemler bu bileşenler üzerinde ayrı ayrı uygulanır.…”

Section: Gi̇ri̇ş (Introduction)unclassified

“…Bu sayede, alışılmadık durumların tespitinde %18'a kadar performans artışı elde edebilmişlerdir. Bu çalışmada, yüksek doğruluk veren hibrit ARIMA-YSA modelini [18], öznitelik tabanlı bir modele çevirerek, geliştirmeyi hedefledik. İstatistiksel yöntemlerle hesaplanan öznitelikler, verilen zaman serisini açıklayıcı değişkenler olarak hibrit yönteme sunulmuştur.…”

Section: Gi̇ri̇ş (Introduction)unclassified

See 3 more Smart Citations

Tek değişkenli zaman serileri tahmini için öznitelik tabanlı hibrit ARIMA-YSA modeli

Büyükşahin

Ertekin

2019

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

Self Cite

View full text Add to dashboard Cite

Highlights:Graphical/Tabular Abstract  A feature-based hybrid ARIMA-ANN method is proposed for time series forecasting.  Our new hybrid method calculates features of a given time series and selects most important ones.  The method achieved better forecasting accuracy than the examined individual and hybrid methods.The architecture of the proposed feature-based hybrid method is indicated in Figure 1. Figure 1 Proposed feature-based hybrid methodPurpose: In this study, it is aimed to improve the high-accuracy hybrid ARIMA-ANN model for univariate time series forecasting by converting it to a feature-based model. Theory and Methods:The proposed method firstly obtains a linear component from a given univariate time series by using moving average (MA) filter. Then, nonlinear residual series, which is the difference between given time series and linear component, is achieved. After that, a set of classical and advanced features is extracted from the observed data and their importance scores are computed by using gradient boosting trees. The most informative features are selected and used in the hybrid method as explanatory variables which lead to improve time series forecasting accuracy results. Results:The performance of the proposed feature-based hybrid method is evaluated on four different time series each which come from different fields and have different statistical characteristics. The proposed method improves the absolute accuracy of the hybrid method that we built on it, between 5.5% and 11.8% in the evaluated datasets. Conclusion:The univariate time series forecasting methods can use only values in the prior time steps to predict next value. They do not take account of underlying structural characteristics of past values which might provide a more meaningful summarized information that lead to yield better approximations in the forecasting. The developed method, extracts features which basically measure the characteristics of the given series. After that, the most explanatory features are selected and used in the hybrid method for improving forecasting performance results. Our experimental results indicate that our feature-based hybrid method gives superior accuracy as compared to well-known existing methods.High prediction accuracies at time series modeling and forecasting is of the utmost importance for a variety of application domains. Many methods have been proposed in the literature to improve time series forecasting accuracy. Those which focus on univariate time series forecasting methods use only the values in the prior time steps to predict the next value. In this study in addition to the historical values, it is aimed to increase the forecasting performance by using extra statistical and structural features which summarize characteristics of the time series. Feature importance scores are determined by gradient boosting trees (GBT).Features with the highest importance score are given as explanatory additional variable to the hybrid ARIMA-ANN model. The evaluation of the developed method is performe...

show abstract

Section: Deneyler (Experiments)unclassified

“…Deneylerde, önerilen yöntemin performans sonuçları, tüm veri setleri için, üzerine inşa edilen hibrit yöntem [18]…”

Section: Deneyler (Experiments)unclassified

Section: Gi̇ri̇ş (Introduction)unclassified

See 2 more Smart Citations

Tek değişkenli zaman serileri tahmini için öznitelik tabanlı hibrit ARIMA-YSA modeli

Büyükşahin

Ertekin

2019

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

Self Cite

View full text Add to dashboard Cite

show abstract

“…Firstly, the exponential smoothing (ES) method [5,6] and the moving average (MA) method [7] are simple and widely used, and performed well in forecasting competitions against more sophisticated approaches. Secondly, the autoregressive integrated moving average (ARIMA) model integrates autoregressive models (AR) and moving average models (MA), and is widely used as a linear time series forecasting method [8,9]. The ARIMA model gives good accuracy in forecasting relatively stationary time series data, but needs a strong assumption that the values of future data are linearly dependent on the values of historical data [10].…”

Section: Introductionmentioning

confidence: 99%

A New Period-Sequential Index Forecasting Algorithm for Time Series Data

et al. 2019

View full text Add to dashboard Cite

A period-sequential index algorithm with sigma-pi neural network technology, which is called the (SPNN-PSI) method, is proposed for the prediction of time series datasets. Using the SPNN-PSI method, the cumulative electricity output (CEO) dataset, Volkswagen sales (VS) dataset, and electric motors exports (EME) dataset are tested. The results show that, in contrast to the moving average (MA), exponential smoothing (ES), and autoregressive integrated moving average (ARIMA) methods, the proposed SPNN-PSI method shows satisfactory forecasting quality due to lower error, and is more suitable for the prediction of time series datasets. It is also concluded that: There is a trend that the higher the correlation coefficient value of the reference historical datasets, the higher the prediction quality of SPNN-PSI method, and a higher value (>0.4) of correlation coefficient for SPNN-PSI method can help to improve occurrence probability of higher forecasting accuracy, and produce more accurate forecasts for the big datasets.

show abstract

Time Series Prediction of Solar Power Generation Using Trend Decomposition

Kavakci,

Cicekdag,

Ertekin

2023

Energy Tech

View full text Add to dashboard Cite

High‐accuracy predictions of future solar power generations are important for monitoring, maintenance, dispatching, and scheduling. The goal of this study is to create a forecasting workflow that increases prediction accuracy independent of the machine learning method and has minimal computational requirements. The proposed trend decomposition method incorporates irradiance and seasonal features as exogenous inputs. In order to extract the linear part of the data, a moving average filter is used. The nonlinear (stable) component of the time series is then calculated by subtracting this linear part from the original data. The stable portion is modeled using several machine learning methods, while the ordinary least squares method is applied to the linear series. By aggregating both results, the final forecast is obtained. The forecasting performances of the machine learning algorithms on unprocessed data are used as baselines for evaluations. Improvements up to 39% in the mean absolute error and up to 31% in the root mean square error metrics are observed compared to the baselines. Experimental results show that the proposed trend decomposition with extrapolation method increases the forecasting performance and generalization capacity of machine learning algorithms.

show abstract

Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition

Cited by 233 publications

References 30 publications

Tek değişkenli zaman serileri tahmini için öznitelik tabanlı hibrit ARIMA-YSA modeli

Tek değişkenli zaman serileri tahmini için öznitelik tabanlı hibrit ARIMA-YSA modeli

A New Period-Sequential Index Forecasting Algorithm for Time Series Data

Time Series Prediction of Solar Power Generation Using Trend Decomposition

Contact Info

Product

Resources

About